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When actors in a social network interact, it usually means they have somegeneral goal towards which they are collaborating. This could be a researchcollaboration in a company or a foursome planning a golf game. We call suchgroups \emph{planning groups}. In many social contexts, it might be possible toobserve the \emph{dyadic interactions} between actors, even if the actors donot explicitly declare what groups they belong too. When groups are notexplicitly declared, we call them \emph{hidden groups}. Our particular focus ishidden planning groups. By virtue of their need to further their goal, theactors within such groups must interact in a manner which differentiates theircommunications from random background communications. In such a case, one caninfer (from these interactions) the composition and structure of the hiddenplanning groups. We formulate the problem of hidden group discovery fromstreaming interaction data, and we propose efficient algorithms for identifyingthe hidden group structures by isolating the hidden group's non-random,planning-related, communications from the random background communications. Wevalidate our algorithms on real data (the Enron email corpus and Blogcommunication data). Analysis of the results reveals that our algorithmsextract meaningful hidden group structures.

Extracting Hidden Groups and their Structure from Streaming Interaction Data